Trial data for precision analysis of a three-dimensional mandibular mechanical advantage
Bibliographic record
Abstract
The data presented in this manuscript describe craniofacial landmark coordinate values, muscle and load moment arm lengths, and mechanical advantage rates for constructing a three-dimensional model of masticatory muscles. Cone-beam computed tomography scans from 30 subjects (aged 12-19 years, 16 females) were used. Thirty-six craniofacial landmarks were identified. Subsequently, the moment arms for 7 muscles and their corresponding load moment arms at incisor and molar positions were determined. Then, the three-dimensional mechanical advantage for each muscle and tooth position was calculated as the ratio of muscle moment arm to load moment arm. This procedure was repeated three times by a main examiner and once by two other examiners. The Friedman test and the square root of the 'method of moments' variance estimator were used to compare data among examiners and calculate random errors, respectively. Although the values for the craniofacial landmark coordinates and biomechanical variables are very close, differences were found between measurements, especially in the interexaminer comparisons. Values served as the basis for reliability (intraclass correlation coefficient) and errors (average mean of absolute differences) analysis in the research paper titled "A three-dimensional method to calculate mechanical advantage in mandibular function: Intra- and interexaminer reliability study," published in the Journal of Orofacial Orthopedics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".